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Semi-Supervised Machine Learning & Deep Learning Models in Crisis-Related Informativeness Classification

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posted on 01.12.2019, 00:00 authored by Alessandro Rennola
This study examines the impact of several state-of-the-art Machine Learning and Deep Learning techniques in the context of semi-supervised disaster-related Twitter mining. The goal is to create a model able to successfully classify informative tweets in the context of natural and human-induced disasters by employing several Machine Learning (Naive Bayes and Support-Vector Machines) and Deep Learning (Convolutional Neural Networks, Bidirectional Long Short-Term Memory) mechanisms. Firstly, we evaluate the performance of supervised instances. Subsequently, the supervised models are extended to assess the impact of semi-supervised techniques (self-training for NB, SVM, CNN; Virtual Adversarial Loss for Bi-LSTM). The accuracy of our Bi-LSTM model peaks at 0.961 in the English dataset, and 0.969 in the Italian dataset. In our knowledge, our semi-supervised learning models for informativeness classification outperform other supervised state-of-the-art models. Finally, our conclusions are drawn as a means to provide a meaningful starting point for future research opportunities.

History

Advisor

Caragea, Cornelia

Chair

Caragea, Cornelia

Department

Computer Science

Degree Grantor

University of Illinois at Chicago

Degree Level

Masters

Degree name

MS, Master of Science

Committee Member

Koyuncu, Erdem Baralis, Elena Maria

Submitted date

December 2019

Thesis type

application/pdf

Language

en

Issue date

29/10/2019